For background, here are some selected quotes from the article:
> "The first part, which you're reading right now, will set up what RL is and why it is fundamentally flawed." > "In the typical model of RL, the agent begins only with knowledge of which actions are possible; it knows nothing else about the world, and it's expected to learn the skill solely by interacting with the environment and receiving rewards after every action it takes." > "how reasonable is it to design AI models based on pure RL if pure RL makes so little intuitive sense?"
To summarize, the article claims that this particular aspect of RL is a "flaw".
I'd suggest it is more useful to call it a design choice. In many cases, this design choice has beneficial properties.
Of course, there are other ways to build learning agents. The field of RL is certainly open to alternatives, including hybrid models and/or relaxing this particular assumption.
I've seen a good number of (popular) articles about RL making rather broad claims, like this article. It appears to me that many of these articles attempt to 'reduce' RL to a smaller/narrower version of itself in order to make their claims. I hope more people start to see that RL is a set of techniques (not a monolith) that can be mixed and matched in many ways for particular applications.